Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach
This addresses the problem of manual CTR measurement for radiologists by providing an automated tool, though it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The researchers tackled automated cardiothoracic ratio (CTR) calculation and cardiomegaly detection from chest X-ray films using a deep learning model, achieving 76.5% of CTR measurements accepted by radiologists without adjustment, which saves time and labor.
We propose an algorithm for calculating the cardiothoracic ratio (CTR) from chest X-ray films. Our approach applies a deep learning model based on U-Net with VGG16 encoder to extract lung and heart masks from chest X-ray images and calculate CTR from the extents of obtained masks. Human radiologists evaluated our CTR measurements, and $76.5\%$ were accepted to be included in medical reports without any need for adjustment. This result translates to a large amount of time and labor saved for radiologists using our automated tools.